In some cases, while working a Pandas dataframe, you may get a kick out of the chance to subset the dataframe by keeping or hanging different segments. In this post, we will see instances of dropping different sections from a Pandas dataframe. We can drop segments in a couple of ways. We will utilize Pandas drop() capacity to figure out how to drop various segments and get a more modest Pandas dataframe.
I realize how to drop segments from an information outline utilizing Python. However, for my concern the informational index is huge, the segments I need to drop are assembled or are fundamentally uniquely fanned out across the section heading hub. Is there a more limited method for cutting or dropping every one of the segments with fewer lines of code as opposed to working it out like how I have done? The manner in which I have done it here works yet I might want a more summed up way.
We should examine how to drop one or different sections in Pandas Dataframe. Drop one or beyond what one segments from a DataFrame can be accomplished in more ways than one. Make a straightforward dataframe with word reference of records, say segment names are A, B, C, D, E.
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What are pandas in Python?
pandas is a python bundle for information control. It has a few capacities for the accompanying information assignments:
- Drop or Keep lines and sections
- Total information by at least one segments
- Sort or reorder information
- Blend or add different dataframes
- String Functions to deal with text information
- DateTime Functions to deal with date or time design segments
# Import pandas package import pandas as pd # create a dictionary with five fields eachdata = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) df |
Output:
Method #1: Drop Columns from a Dataframe using drop() method.
Remove specific single column.
# Import pandas package import pandas as pd # create a dictionary with five fields each data = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) # Remove column name 'A'df.drop(['A'], axis = 1) |
Output:
Remove specific multiple columns.
# Import pandas package import pandas as pd # create a dictionary with five fields eachdata = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) # Remove two columns name is 'C' and 'D'df.drop(['C', 'D'], axis = 1) # df.drop(columns =['C', 'D']) |
Output:
Remove columns as based on column index.
# Import pandas package import pandas as pd # create a dictionary with five fields eachdata = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) # Remove three columns as index basedf.drop(df.columns[[0, 4, 2]], axis = 1, inplace = True) df |
Output:
Method #2: Drop Columns from a Dataframe using iloc[] and drop() method.
Remove all columns between a specific column to another columns.
# Import pandas package import pandas as pd# create a dictionary with five fields each data = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) # Remove all columns between column index 1 to 3df.drop(df.iloc[:, 1:3], inplace = True, axis = 1) df |
Output:
Method #3: Drop Columns from a Dataframe using ix() and drop() method.
Remove all columns between a specific column name to another columns name.
# Import pandas package import pandas as pd # create a dictionary with five fields each data = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) # Remove all columns between column name 'B' to 'D'df.drop(df.ix[:, 'B':'D'].columns, axis = 1) |
Output:
Method #4: Drop Columns from a Dataframe using loc[] and drop() method.
Remove all columns between a specific column name to another columns name.
# Import pandas package import pandas as pd # create a dictionary with five fields each data = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) # Remove all columns between column name 'B' to 'D'df.drop(df.loc[:, 'B':'D'].columns, axis = 1) |
Output:
Note: Different loc() and iloc() is iloc() exclude last column range element.\
Method #5: Drop Columns from a Dataframe by iterative way.
Remove all columns between a specific column name to another columns name.
# Import pandas package import pandas as pd # create a dictionary with five fields each data = { 'A':['A1', 'A2', 'A3', 'A4', 'A5'], 'B':['B1', 'B2', 'B3', 'B4', 'B5'], 'C':['C1', 'C2', 'C3', 'C4', 'C5'], 'D':['D1', 'D2', 'D3', 'D4', 'D5'], 'E':['E1', 'E2', 'E3', 'E4', 'E5'] } # Convert the dictionary into DataFrame df = pd.DataFrame(data)for col in df.columns: if 'A' in col: del df[col] df |
Output:
